| Abstract | Vast amounts of clinical information are generated
daily on patients in the health care setting.
Increasingly, this information is collected and stored
for its potential utility in advancing health care.
Knowledge-based systems, for example, might be able
to apply rules to the collected data to determine
whether a patient has a certain condition. Often,
however, the underlying knowledge needed to write
such rules is not well understood. How could these
clinical data be useful then? Use of machine learning
is one answer. We present a pipeline for discovering
the knowledge needed for event detection in medical
time-series data. We demonstrate how this process
can be applied in the development of intelligent patient
monitoring for the intensive care unit (ICU).
Specifically, we develop a system for detecting "true
alarm" situations in the ICU, where currently as many
as 86% of bedside monitor alarms are false. [Sample data in this paper was from a neonatal ICU.] |